Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Rachel Woods-Robinson, Matthew K. Horton, Kristin A. Persson, ,
| Summary:
Conventionally, high-throughput computational materials searches start from
an input set of bulk compounds extracted from material databases, and this set
is screened for candidate materials for specific applications. In contrast,
many functional materials, and especially semiconductors, are heavily
engineered alloys or solid solutions of multiple compounds rather than a single
bulk compound. To improve our ability to design functional materials, in this
work we propose a framework and open-source code to automatically construct
possible "alloy pairs" and "alloy systems" and detect "alloy members" from a
set of existing, experimental or calculated ordered compounds, without
requiring any additional metadata beyond their crystal structure. We provide
analysis tools to estimate stability across each alloy. As a demonstration, we
apply this framework to all inorganic materials in the Materials Project
database to create a new database of over 600,000 unique alloy pair entries
that can then be used in materials discovery studies to search for materials
with tunable properties. This new database has been incorporated into the
Materials Project website and linked with corresponding material identifiers
for any user to query and explore. Using an example of screening for p-type
transparent conducting materials, we demonstrate how using this methodology
reveals candidate material systems that might otherwise have been excluded by a
traditional screening. This work lays a foundation from which materials
databases can go beyond stoichiometric compounds, and approach a more realistic
description of compositionally tunable materials.
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